20,432 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Pedestrian attribute inference is a demanding problem in visual surveillance
that can facilitate person retrieval, search and indexing. To exploit semantic
relations between attributes, recent research treats it as a multi-label image
classification task. The visual cues hinting at attributes can be strongly
localized and inference of person attributes such as hair, backpack, shorts,
etc., are highly dependent on the acquired view of the pedestrian. In this
paper we assert this dependence in an end-to-end learning framework and show
that a view-sensitive attribute inference is able to learn better attribute
predictions. Our proposed model jointly predicts the coarse pose (view) of the
pedestrian and learns specialized view-specific multi-label attribute
predictions. We show in an extensive evaluation on three challenging datasets
(PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute
prediction model provides competitive performance and improves on the published
state-of-the-art on these datasets.Comment: accepted BMVC 201
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